GE Aviation - Remaining Useful Life Analysis

Part 1 - Data Preparation

Getting Started

Connecting to the database

pacman::p_load(RMySQL, tidyverse, lubridate, geosphere, magrittr, kableExtra)

mysqlconnection = dbConnect(
  RMySQL::MySQL(),
  dbname='gedata',
  host='mysql.fsb.miamioh.edu',
  port=3306,
  user='fsbstud',
  password='fsb4you')

dbListTables(mysqlconnection)

[1] "engine_data_aic"          "engine_data_axm"         
[3] "engine_data_fron"         "engine_data_pgt"         
[5] "esn_rul"                  "lkp_airport_codes_t"     
[7] "manufacturing_sql_by_esn"

Reading the table

esn_rul = dbReadTable(mysqlconnection, "esn_rul")
engine_data_aic = dbReadTable(mysqlconnection, "engine_data_aic")
engine_data_axm = dbReadTable(mysqlconnection, "engine_data_axm")
engine_data_fron = dbReadTable(mysqlconnection, "engine_data_fron")
engine_data_pgt = dbReadTable(mysqlconnection, "engine_data_pgt")
lkp_airport_codes_t = dbReadTable(mysqlconnection, "lkp_airport_codes_t")
manufacturing_sql_by_esn = dbReadTable(mysqlconnection, "manufacturing_sql_by_esn")

Top observations for each dataset

engine_data_aic

dataset	esn	unit	flight_cycle	datetime	operator	depart_icao	destination_icao	hpc_eff_mod	hpc_flow_mod	tra	t2	t24	t30	t50	p2	p15	p30	nf	nc	epr	ps30	phi	nrf	nrc	bpr	farb	htbleed	nf_dmd	pcnfr_dmd	w31	w32
test_FD001	999120	20	1	2018-02-01T13:47:42.000Z	AIC	LFBO	LFBO	0.0011	-5e-04	100	518.67	182.59	1588.38	1397.780	14.62	21.61	554.43	2388.07	9072.24	1.3	47.40	521.88	2388.06	8147.58	8.3923	0.03	393	2388	100	38.76	23.4970
test_FD001	999120	20	2	2018-02-01T18:22:24.000Z	AIC	LFBO	VIDP	-0.0001	-3e-04	100	518.67	182.56	1590.73	1405.880	14.62	21.61	553.62	2388.06	9059.50	1.3	47.32	522.03	2388.06	8151.74	8.4385	0.03	392	2388	100	38.90	23.4240
test_FD001	999120	20	3	2018-02-08T00:46:00.000Z	AIC	VIDP	VABB	-0.0015	3e-04	100	518.67	182.54	1590.70	1401.310	14.62	21.60	554.24	2388.08	9064.83	1.3	47.19	521.80	2388.03	8146.37	8.4234	0.03	392	2388	100	38.96	23.4460
test_FD001	999120	20	4	2018-02-08T03:58:00.000Z	AIC	VABB	VIDP	-0.0022	1e-04	100	518.67	183.01	1588.91	1403.280	14.62	21.61	553.79	2388.06	9058.04	1.3	47.28	522.25	2388.06	8144.65	8.3955	0.03	391	2388	100	38.94	23.3412
test_FD001	999120	20	5	2018-02-08T07:14:00.000Z	AIC	VIDP	VABB	-0.0064	-1e-04	100	518.67	182.73	1588.43	1406.460	14.62	21.61	553.90	2388.06	9064.03	1.3	47.42	522.17	2388.04	8146.81	8.4371	0.03	393	2388	100	38.94	23.4452
test_FD001	999120	20	6	2018-02-08T11:23:00.000Z	AIC	VABB	VOCI	0.0002	5e-04	100	518.67	182.64	1583.41	1410.055	14.62	21.61	554.59	2388.06	9065.79	1.3	47.21	521.51	2388.00	8144.26	8.4084	0.03	394	2388	100	39.11	23.2680

engine_data_axm

dataset	esn	unit	flight_cycle	datetime	operator	depart_icao	destination_icao	hpc_eff_mod	hpc_flow_mod	tra	t2	t24	t30	t50	p2	p15	p30	nf	nc	epr	ps30	phi	nrf	nrc	bpr	farb	htbleed	nf_dmd	pcnfr_dmd	w31	w32
train_FD001	999062	62	1	2018-03-01T08:25:50.000Z	AXM	WMKK	VHHH	0.0026	1e-04	100	518.67	642.78	1595.36	1410.055	14.62	21.61	553.33	2388.19	9060.39	1.3	47.60	521.77	2388.15	8139.18	8.4882	0.03	393	2388	100	38.77	23.2630
train_FD001	999062	62	2	2018-03-01T13:03:43.000Z	AXM	VHHH	WMKK	-0.0010	5e-04	100	518.67	642.50	1591.94	1411.070	14.62	21.61	552.99	2388.12	9060.53	1.3	47.65	521.75	2388.13	8136.55	8.4135	0.03	393	2388	100	38.67	23.3297
train_FD001	999062	62	3	2018-03-01T22:26:54.000Z	AXM	WMKK	VYYY	0.0031	0e+00	100	518.67	643.11	1598.06	1410.285	14.62	21.61	552.63	2388.10	9060.52	1.3	47.59	521.22	2388.06	8134.15	8.4183	0.03	393	2388	100	38.76	23.1726
train_FD001	999062	62	4	2018-03-01T23:33:03.000Z	AXM	VYYY	VYYY	-0.0023	-2e-04	100	518.67	642.68	1591.31	1410.655	14.62	21.61	553.47	2388.15	9057.44	1.3	47.50	521.20	2388.11	8137.75	8.4488	0.03	392	2388	100	38.99	23.3329
train_FD001	999062	62	5	2018-03-02T02:35:17.000Z	AXM	VYYY	WMKK	-0.0033	4e-04	100	518.67	642.24	1590.59	1410.515	14.62	21.61	553.95	2388.14	9050.12	1.3	47.62	521.38	2388.05	8140.60	8.4512	0.03	393	2388	100	38.84	23.2909
train_FD001	999062	62	6	2018-03-02T09:08:00.000Z	AXM	ZPPP	WMKK	-0.0005	-4e-04	100	518.67	642.25	1590.12	1413.290	14.62	21.61	553.16	2388.15	9056.04	1.3	47.49	521.41	2388.16	8134.03	8.4759	0.03	393	2388	100	38.94	23.2215

engine_data_fron

dataset	esn	unit	flight_cycle	datetime	operator	depart_icao	destination_icao	hpc_eff_mod	hpc_flow_mod	tra	t2	t24	t30	t50	p2	p15	p30	nf	nc	epr	ps30	phi	nrf	nrc	bpr	farb	htbleed	nf_dmd	pcnfr_dmd	w31	w32
train_FD001	999050	50	1	2018-01-06T12:01:09.000Z	FRON	KMCO	KMSY	-0.0029	-2e-04	100	518.67	642.66	1591.79	1401.30	14.62	21.60	554.60	2388.01	9064.12	1.3	47.47	521.68	2388.06	8151.49	8.4158	0.03	393	2388	100	38.80	23.3016
train_FD001	999050	50	2	2018-01-06T13:41:00.000Z	FRON	KMSY	KSAT	-0.0002	-5e-04	100	518.67	642.28	1587.84	1404.96	14.62	21.61	553.60	2388.06	9065.83	1.3	47.33	522.12	2388.07	8142.72	8.4467	0.03	392	2388	100	38.99	23.3440
train_FD001	999050	50	3	2018-01-06T14:41:18.000Z	FRON	KMSY	KSAT	-0.0010	-5e-04	100	518.67	642.21	1586.89	1404.47	14.62	21.61	554.31	2388.05	9065.63	1.3	47.48	521.96	2388.05	8139.14	8.4424	0.03	393	2388	100	38.91	23.3190
train_FD001	999050	50	4	2018-01-06T16:14:00.000Z	FRON	KSAT	KSAN	-0.0061	-2e-04	100	518.67	643.19	1587.36	1398.90	14.62	21.61	554.35	2388.07	9059.91	1.3	47.30	522.31	2388.04	8145.16	8.4504	0.03	393	2388	100	38.95	23.3161
train_FD001	999050	50	5	2018-01-06T17:12:52.000Z	FRON	KSAT	KSAN	-0.0002	1e-04	100	518.67	642.47	1584.96	1406.08	14.62	21.61	554.03	2388.00	9073.29	1.3	47.44	522.05	2388.05	8145.35	8.3822	0.03	392	2388	100	38.83	23.3256
train_FD001	999050	50	6	2018-01-06T20:21:00.000Z	FRON	KSAN	KSAT	-0.0003	-3e-04	100	518.67	641.82	1585.30	1399.30	14.62	21.61	554.38	2388.02	9068.48	1.3	47.13	522.17	2388.04	8144.13	8.4180	0.03	393	2388	100	38.80	23.4777

engine_data_pgt

dataset	esn	unit	flight_cycle	datetime	operator	depart_icao	destination_icao	hpc_eff_mod	hpc_flow_mod	tra	t2	t24	t30	t50	p2	p15	p30	nf	nc	epr	ps30	phi	nrf	nrc	bpr	farb	htbleed	nf_dmd	pcnfr_dmd	w31	w32
train_FD001	999056	56	1	2018-01-01T12:33:13.000Z	PGT	LTBJ	LTCR	0.0012	-4e-04	100	518.67	642.75	1586.44	1412.720	14.62	21.61	552.68	2388.10	9059.10	1.3	47.72	521.18	2388.13	8136.92	8.4412	0.03	395	2388	100	38.81	23.2391
train_FD001	999056	56	2	2018-01-01T15:40:21.000Z	PGT	LTCR	LTBJ	0.0012	-4e-04	100	518.67	642.47	1584.96	1410.405	14.62	21.61	552.90	2388.12	9057.99	1.3	47.42	520.82	2388.08	8133.11	8.4461	0.03	394	2388	100	38.82	23.3340
train_FD001	999056	56	3	2018-01-01T18:23:01.000Z	PGT	LTBJ	LTFJ	0.0026	5e-04	100	518.67	642.52	1587.64	1403.700	14.62	21.61	553.52	2388.13	9054.91	1.3	47.48	521.70	2388.12	8136.86	8.4357	0.03	394	2388	100	38.89	23.2844
train_FD001	999056	56	4	2018-01-01T20:11:10.000Z	PGT	LTFJ	LTCG	0.0034	-2e-04	100	518.67	642.51	1587.80	1410.585	14.62	21.61	553.60	2388.13	9045.30	1.3	47.49	522.06	2388.18	8132.53	8.4411	0.03	394	2388	100	38.79	23.3204
train_FD001	999056	56	5	2018-01-02T03:10:50.000Z	PGT	LTCG	LTFJ	0.0024	-1e-04	100	518.67	643.08	1593.15	1401.460	14.62	21.61	553.45	2388.03	9046.37	1.3	47.67	521.36	2388.16	8133.47	8.4824	0.03	394	2388	100	39.00	23.3592
train_FD001	999056	56	6	2018-01-02T06:09:11.000Z	PGT	LTFJ	LTCN	0.0010	-2e-04	100	518.67	642.52	1589.19	1408.880	14.62	21.61	553.22	2388.12	9054.19	1.3	47.77	520.98	2388.13	8132.14	8.4382	0.03	393	2388	100	38.83	23.2568

esn_rul

esn	rul
999182	9
999115	93
999184	63
999113	104
999175	123
999197	95

lkp_airport_codes_t

airport_icao	latitude	longitude
EDDN	49.499	11.078
KSAT	29.534	-98.469
KSLC	40.788	-111.978
KTYS	35.811	-83.994
LTAR	39.814	36.903
LWSK	41.962	21.621

manufacturing_sql_by_esn

esn	X44321P02_op016_median_first	X44321P02_op420_median_first	X54321P01_op116_median_first	X54321P01_op220_median_first	X65421P11_op232_median_first	X65421P11_op630_median_first
999016	26.85684	11.77815	27.10170	22.76247	117.6866	152.3637
999049	19.58206	10.45221	33.77607	21.28657	193.9467	235.9931
999135	24.94090	10.08180	21.94967	28.45795	140.1707	190.1172
999140	21.43138	14.11467	33.67310	29.35572	203.6650	150.0929
999063	25.12926	15.95785	27.14931	27.86627	143.6061	208.8172
999089	19.19329	13.40339	26.16745	29.49466	217.1039	241.2999

Joining the data sets

First, the 4 engine datasets from the 4 operators were appended to create the engine_health dataset.

engine_health = rbind(engine_data_aic, engine_data_axm, engine_data_fron, engine_data_pgt)

Next, engine_health was merged with the remaining datasets to create a collective data frame df, specifically:

• manufacturing_sql_by_esn contains part numbers and operations of each engine
• lkp_airport_codes_t contains the coordinates of each airport used to calculate the flight distance for each flight.
  • After merging, the coordinates columns for depart_icao were renamed as depart_latitude and depart_longitude, similarly for destination_icao.
• esn_rul contains key-value pairs of esn and RUL.

df = left_join(engine_health, manufacturing_sql_by_esn, by = 'esn')

df = left_join(df, lkp_airport_codes_t, by=c('depart_icao'='airport_icao'))
colnames(df)[which(names(df) == "latitude")]= 'depart_latitude'
colnames(df)[which(names(df) == "longitude")]= 'depart_longitude'

df = left_join(df, lkp_airport_codes_t, by=c('destination_icao'='airport_icao'))
colnames(df)[which(names(df) == "latitude")]= 'destination_latitude'
colnames(df)[which(names(df) == "longitude")]= 'destination_longitude'

df = left_join(df, esn_rul, by = 'esn')

Calculate Distance

The flight distance was estimated (in kilometers) from the provided coordinates of departure and destination locations using the distVincentyEllipsoid function from the geosphere package, which calculates the shortest distance between 2 points (the great-circle-distance) according to the Vincenty (ellipsoid) method. Please note that this estimate does not necessarily reflect the real distance of a particular flight due to the lack of specific information on the flight path.

The columns on location information were dropped afterward as they were no longer informative, also to reduce the number of variables to consider.

df$distance = mapply(function(long1, lat1, long2, lat2) distVincentyEllipsoid(c(long1, lat1), c(long2, lat2))/1000, df$depart_longitude, df$depart_latitude,df$destination_longitude, df$destination_latitude)

# Drop location columns
df %<>% select(-c('depart_icao','destination_icao', 
                  'depart_latitude','depart_longitude',
                  'destination_latitude','destination_longitude'))

Check for tidy, technically correct, and consistent data

• Fill in NA values for blank cells that were not null.

  • From my first validation run, I noticed that there were missing values in distance, as well as the latitude and longitude columns. I then investigated these observations more closely and figured there were blank cells in the ICAO columns (hence the NA values in the coordinates columns and calculated distance). These cells appeared to be non-null because they might contain empty strings or blank space.

  • Therefore, this step was to make sure any columns with missing values would actually be indicated as such in the validation report.

• Change variable types where necessary:

  • datetime as POSIXct

  • dataset, unit, operator as factor

  • tra, htbleed, nf_dmd, and pcnfr_dmd as numeric

glimpse(df) ## quick look at the data types

Rows: 30,004
Columns: 38
$ dataset                      <chr> "test_FD001", "test_FD001", "test_FD001",~
$ esn                          <int> 999120, 999120, 999120, 999120, 999120, 9~
$ unit                         <int> 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 2~
$ flight_cycle                 <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13~
$ datetime                     <chr> "2018-02-01T13:47:42.000Z", "2018-02-01T1~
$ operator                     <chr> "AIC", "AIC", "AIC", "AIC", "AIC", "AIC",~
$ hpc_eff_mod                  <dbl> 0.0011, -0.0001, -0.0015, -0.0022, -0.006~
$ hpc_flow_mod                 <dbl> -5e-04, -3e-04, 3e-04, 1e-04, -1e-04, 5e-~
$ tra                          <int> 100, 100, 100, 100, 100, 100, 100, 100, 1~
$ t2                           <dbl> 518.67, 518.67, 518.67, 518.67, 518.67, 5~
$ t24                          <dbl> 182.59, 182.56, 182.54, 183.01, 182.73, 1~
$ t30                          <dbl> 1588.38, 1590.73, 1590.70, 1588.91, 1588.~
$ t50                          <dbl> 1397.780, 1405.880, 1401.310, 1403.280, 1~
$ p2                           <dbl> 14.62, 14.62, 14.62, 14.62, 14.62, 14.62,~
$ p15                          <dbl> 21.61, 21.61, 21.60, 21.61, 21.61, 21.61,~
$ p30                          <dbl> 554.43, 553.62, 554.24, 553.79, 553.90, 5~
$ nf                           <dbl> 2388.07, 2388.06, 2388.08, 2388.06, 2388.~
$ nc                           <dbl> 9072.24, 9059.50, 9064.83, 9058.04, 9064.~
$ epr                          <dbl> 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1~
$ ps30                         <dbl> 47.40, 47.32, 47.19, 47.28, 47.42, 47.21,~
$ phi                          <dbl> 521.88, 522.03, 521.80, 522.25, 522.17, 5~
$ nrf                          <dbl> 2388.06, 2388.06, 2388.03, 2388.06, 2388.~
$ nrc                          <dbl> 8147.58, 8151.74, 8146.37, 8144.65, 8146.~
$ bpr                          <dbl> 8.3923, 8.4385, 8.4234, 8.3955, 8.4371, 8~
$ farb                         <dbl> 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,~
$ htbleed                      <int> 393, 392, 392, 391, 393, 394, 391, 392, 3~
$ nf_dmd                       <int> 2388, 2388, 2388, 2388, 2388, 2388, 2388,~
$ pcnfr_dmd                    <int> 100, 100, 100, 100, 100, 100, 100, 100, 1~
$ w31                          <dbl> 38.76, 38.90, 38.96, 38.94, 38.94, 39.11,~
$ w32                          <dbl> 23.4970, 23.4240, 23.4460, 23.3412, 23.44~
$ X44321P02_op016_median_first <dbl> 23.25222, 23.25222, 23.25222, 23.25222, 2~
$ X44321P02_op420_median_first <dbl> 14.54578, 14.54578, 14.54578, 14.54578, 1~
$ X54321P01_op116_median_first <dbl> 22.15643, 22.15643, 22.15643, 22.15643, 2~
$ X54321P01_op220_median_first <dbl> 29.89512, 29.89512, 29.89512, 29.89512, 2~
$ X65421P11_op232_median_first <dbl> 188.632, 188.632, 188.632, 188.632, 188.6~
$ X65421P11_op630_median_first <dbl> 231.0214, 231.0214, 231.0214, 231.0214, 2~
$ rul                          <int> 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1~
$ distance                     <dbl> 0.0000, 6783.8984, 1134.9964, 1134.9964, ~

df %<>% mutate_all(na_if,"") ## replace "" with NA

## Change variable types
df$datetime = ymd_hms(df$datetime)

df = mutate(df, across(.cols = c(unit), .fns = as.character))
df = mutate(df, across(.cols = c(dataset, unit, operator), .fns = as.factor))
df = mutate(df, across(.cols = c(tra, htbleed, nf_dmd, pcnfr_dmd), .fns= as.numeric))

Data validation

pacman::p_load(pointblank)

# Step 1
act = action_levels(warn_at = 0.01, notify_at = 0.01)

# Step 2
agent = create_agent(tbl = df, actions = act)

# Step 3
agent %<>% 
  ## technically correct checks
  col_is_posix(columns = 'datetime') %>% 
  col_is_factor(columns = vars(dataset, unit, operator)) %>% 
  col_is_numeric(columns = -c(1,2,3,4,5,6,37)) %>%
  col_is_integer(columns = vars(flight_cycle,rul)) %>% 
  ## consistency checks
  col_vals_not_null(columns = c(1:ncol(df))) %>% 
  col_vals_gte(columns = vars(t2, t24, t30, t50, nf, nc, phi, nrf, nrc, w31, w32, distance, rul,
                              X44321P02_op016_median_first, X44321P02_op420_median_first, X54321P01_op116_median_first,
                              X54321P01_op220_median_first, X65421P11_op232_median_first, X65421P11_op630_median_first), 
               value = 0)

# (4) Eval
results = interrogate(agent)
results  

Pointblank Validation
[2022-12-05|14:34:28] data frame dfWARN 0.01 STOP — NOTIFY 0.01
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	1	col_is_posix()	▮datetime	—		✓	1	1 1.00	0 0.00	○	—	○	—
	2	col_is_factor()	▮dataset	—		✓	1	1 1.00	0 0.00	○	—	○	—
	3	col_is_factor()	▮unit	—		✓	1	1 1.00	0 0.00	○	—	○	—
	4	col_is_factor()	▮operator	—		✓	1	1 1.00	0 0.00	○	—	○	—
	5	col_is_numeric()	▮hpc_eff_mod	—		✓	1	1 1.00	0 0.00	○	—	○	—
	6	col_is_numeric()	▮hpc_flow_mod	—		✓	1	1 1.00	0 0.00	○	—	○	—
	7	col_is_numeric()	▮tra	—		✓	1	1 1.00	0 0.00	○	—	○	—
	8	col_is_numeric()	▮t2	—		✓	1	1 1.00	0 0.00	○	—	○	—
	9	col_is_numeric()	▮t24	—		✓	1	1 1.00	0 0.00	○	—	○	—
	10	col_is_numeric()	▮t30	—		✓	1	1 1.00	0 0.00	○	—	○	—
	11	col_is_numeric()	▮t50	—		✓	1	1 1.00	0 0.00	○	—	○	—
	12	col_is_numeric()	▮p2	—		✓	1	1 1.00	0 0.00	○	—	○	—
	13	col_is_numeric()	▮p15	—		✓	1	1 1.00	0 0.00	○	—	○	—
	14	col_is_numeric()	▮p30	—		✓	1	1 1.00	0 0.00	○	—	○	—
	15	col_is_numeric()	▮nf	—		✓	1	1 1.00	0 0.00	○	—	○	—
	16	col_is_numeric()	▮nc	—		✓	1	1 1.00	0 0.00	○	—	○	—
	17	col_is_numeric()	▮epr	—		✓	1	1 1.00	0 0.00	○	—	○	—
	18	col_is_numeric()	▮ps30	—		✓	1	1 1.00	0 0.00	○	—	○	—
	19	col_is_numeric()	▮phi	—		✓	1	1 1.00	0 0.00	○	—	○	—
	20	col_is_numeric()	▮nrf	—		✓	1	1 1.00	0 0.00	○	—	○	—
	21	col_is_numeric()	▮nrc	—		✓	1	1 1.00	0 0.00	○	—	○	—
	22	col_is_numeric()	▮bpr	—		✓	1	1 1.00	0 0.00	○	—	○	—
	23	col_is_numeric()	▮farb	—		✓	1	1 1.00	0 0.00	○	—	○	—
	24	col_is_numeric()	▮htbleed	—		✓	1	1 1.00	0 0.00	○	—	○	—
	25	col_is_numeric()	▮nf_dmd	—		✓	1	1 1.00	0 0.00	○	—	○	—
	26	col_is_numeric()	▮pcnfr_dmd	—		✓	1	1 1.00	0 0.00	○	—	○	—
	27	col_is_numeric()	▮w31	—		✓	1	1 1.00	0 0.00	○	—	○	—
	28	col_is_numeric()	▮w32	—		✓	1	1 1.00	0 0.00	○	—	○	—
	29	col_is_numeric()	▮X44321P02_op016_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	30	col_is_numeric()	▮X44321P02_op420_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	31	col_is_numeric()	▮X54321P01_op116_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	32	col_is_numeric()	▮X54321P01_op220_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	33	col_is_numeric()	▮X65421P11_op232_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	34	col_is_numeric()	▮X65421P11_op630_median_first	—		✓	1	1 1.00	0 0.00	○	—	○	—
	35	col_is_numeric()	▮distance	—		✓	1	1 1.00	0 0.00	○	—	○	—
	36	col_is_integer()	▮flight_cycle	—		✓	1	1 1.00	0 0.00	○	—	○	—
	37	col_is_integer()	▮rul	—		✓	1	1 1.00	0 0.00	○	—	○	—
	38	col_vals_not_null()	▮dataset	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	39	col_vals_not_null()	▮esn	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	40	col_vals_not_null()	▮unit	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	41	col_vals_not_null()	▮flight_cycle	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	42	col_vals_not_null()	▮datetime	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	43	col_vals_not_null()	▮operator	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	44	col_vals_not_null()	▮hpc_eff_mod	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	45	col_vals_not_null()	▮hpc_flow_mod	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	46	col_vals_not_null()	▮tra	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	47	col_vals_not_null()	▮t2	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	48	col_vals_not_null()	▮t24	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	49	col_vals_not_null()	▮t30	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	50	col_vals_not_null()	▮t50	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	51	col_vals_not_null()	▮p2	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	52	col_vals_not_null()	▮p15	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	53	col_vals_not_null()	▮p30	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	54	col_vals_not_null()	▮nf	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	55	col_vals_not_null()	▮nc	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	56	col_vals_not_null()	▮epr	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	57	col_vals_not_null()	▮ps30	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	58	col_vals_not_null()	▮phi	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	59	col_vals_not_null()	▮nrf	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	60	col_vals_not_null()	▮nrc	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	61	col_vals_not_null()	▮bpr	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	62	col_vals_not_null()	▮farb	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	63	col_vals_not_null()	▮htbleed	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	64	col_vals_not_null()	▮nf_dmd	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	65	col_vals_not_null()	▮pcnfr_dmd	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	66	col_vals_not_null()	▮w31	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	67	col_vals_not_null()	▮w32	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	68	col_vals_not_null()	▮X44321P02_op016_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	69	col_vals_not_null()	▮X44321P02_op420_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	70	col_vals_not_null()	▮X54321P01_op116_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	71	col_vals_not_null()	▮X54321P01_op220_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	72	col_vals_not_null()	▮X65421P11_op232_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	73	col_vals_not_null()	▮X65421P11_op630_median_first	—		✓	30K	30K 1.00	0 0.00	○	—	○	—
	74	col_vals_not_null()	▮rul	—		✓	30K	13K 0.44	17K 0.56	●	—	●
	75	col_vals_not_null()	▮distance	—		✓	30K	29K 0.97	1K 0.03	●	—	●
	76	col_vals_gte()	▮t2	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	77	col_vals_gte()	▮t24	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	78	col_vals_gte()	▮t30	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	79	col_vals_gte()	▮t50	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	80	col_vals_gte()	▮nf	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	81	col_vals_gte()	▮nc	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	82	col_vals_gte()	▮phi	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	83	col_vals_gte()	▮nrf	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	84	col_vals_gte()	▮nrc	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	85	col_vals_gte()	▮w31	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	86	col_vals_gte()	▮w32	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	87	col_vals_gte()	▮distance	0		✓	30K	29K 0.97	1K 0.03	●	—	●
	88	col_vals_gte()	▮rul	0		✓	30K	13K 0.44	17K 0.56	●	—	●
	89	col_vals_gte()	▮X44321P02_op016_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	90	col_vals_gte()	▮X44321P02_op420_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	91	col_vals_gte()	▮X54321P01_op116_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	92	col_vals_gte()	▮X54321P01_op220_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	93	col_vals_gte()	▮X65421P11_op232_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
	94	col_vals_gte()	▮X65421P11_op630_median_first	0		✓	30K	30K 1.00	0 0.00	○	—	○	—
2022-12-05 14:34:29 EST 2.0 s 2022-12-05 14:34:31 EST

Imputation

From the Pointblank Validation above, we can see that distance has a little over 3% of missing values, thus imputation is necessary before constructing a predictive model.

median_distance = median(df$distance, na.rm = T)
df$distance = replace_na(df$distance, median_distance)

Exclude observations missing RUL

In order to create a regression model, the response variable must not be null. Therefore, observations that did not have a RUL for training a model were dropped.

df %<>% drop_na(rul) 

Aggregate Data

Given the insufficient information about the health status of each engine, the data was aggregated to the last flight cycle to capture the latest or averaged measures for RUL prediction.

For each engine, most measures, such as temperature and pressure, were averaged across all flight cycles to account for changes (both degradation and additional maintenance) in between flights. distance was aggregated as total to reflect the accumulated traveled distance.

hpc_eff_mod and hpc_flow_mod were input variables of the simulation that generated the raw data, so they were not included in the aggregation.

df %<>% group_by(dataset, esn, unit, operator) %>% 
        summarize(last_flight_cycle = max(flight_cycle),
                  last_datetime = max(datetime),
                  mean_tra = mean(tra),
                  mean_t2 = mean(t2), mean_t24 = mean(t24), mean_t30 = mean(t30), mean_t50 = mean(t50),
                  mean_p2 = mean(p2), mean_p15 = mean(p15), mean_p30 = mean(p30),
                  mean_nf = mean(nf), mean_nc = mean(nc),
                  mean_epr = mean(epr), mean_ps30 = mean(ps30), mean_phi = mean(phi),
                  mean_nrf = mean(nrf), mean_nrc = mean(nrc), mean_bpr = mean(bpr),
                  mean_farb = mean(farb), mean_htbleed = mean(htbleed),
                  mean_nf_dmd = mean(nf_dmd), mean_pcnfr_dmd = mean(pcnfr_dmd), 
                  mean_w31 = mean(w31), mean_w32 = mean(w32),
                  mean_X44321P02_op016 = mean(X44321P02_op016_median_first), mean_X44321P02_op420 = mean(X44321P02_op420_median_first),
                  mean_X54321P01_op116 = mean(X54321P01_op116_median_first), mean_X54321P01_op220 = mean(X54321P01_op220_median_first),
                  mean_X65421P11_op232 = mean(X65421P11_op232_median_first), mean_X65421P11_op630 = mean(X65421P11_op630_median_first),
                  total_distance = sum(distance),
                  rul = min(rul))

`summarise()` has grouped output by 'dataset', 'esn', 'unit'. You can override
using the `.groups` argument.

Export Data

The data was then exported for later use in the project.

write_csv(df, 'ge_data.csv')